38 citations · 125 across the 49 of their papers we have counts for
8 papers · 1 filter
Contextual Feature Selection with Conditional Stochastic Gates
Ram Dyuthi Sristi, Ofir Lindenbaum, Shira Lifshitz +4
Feature selection is a crucial tool in machine learning and is widely applied across various scientific disciplines. Traditional supervised methods generally identify a universal s…
On a Generalization of Wasserstein Distance and the Beckmann Problem to Connection Graphs
Sawyer Robertson, Dhruv Kohli, Gal Mishne +1
We propose a model of optimal parallel transport between vector fields on a connection graph, which consists of a weighted graph along with a map from its edges to an orthogonal gr…
Random Walks, Conductance, and Resistance for the Connection Graph Laplacian
Alexander Cloninger, Gal Mishne, Andreas Oslandsbotn +3
We investigate the concept of effective resistance in connection graphs, expanding its traditional application from undirected graphs. We propose a robust definition of effective r…
Semi-Supervised Laplace Learning on Stiefel Manifolds
Chester Holtz, Pengwen Chen, Alexander Cloninger +2
Motivated by the need to address the degeneracy of canonical Laplace learning algorithms in low label rates, we propose to reformulate graph-based semi-supervised learning as a non…
Graph Laplacian Learning with Exponential Family Noise
Changhao Shi, Gal Mishne
Graph signal processing (GSP) is a prominent framework for analyzing signals on non-Euclidean domains. The graph Fourier transform (GFT) uses the combinatorial graph Laplacian matr…
SiBBlInGS: Similarity-driven Building-Block Inference using Graphs across States
Noga Mudrik, Gal Mishne, Adam S. Charles
Time series data across scientific domains are often collected under distinct states (e.g., tasks), wherein latent processes (e.g., biological factors) create complex inter- and in…
Hyperbolic Diffusion Embedding and Distance for Hierarchical Representation Learning
Ya-Wei Eileen Lin, Ronald R. Coifman, Gal Mishne +1
Finding meaningful representations and distances of hierarchical data is important in many fields. This paper presents a new method for hierarchical data embedding and distance. Ou…
Non-degenerate Rigid Alignment in a Patch Framework
Dhruv Kohli, Gal Mishne, Alexander Cloninger
Given a set of overlapping local views (patches) of a dataset, we consider the problem of finding a rigid alignment of the views that minimizes a -norm based alignment error. In…